Othman A. Karim

Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation

This paper discusses the accuracy performance of training, validation and prediction of monthly water quality parameters utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS). This model was used to analyse the historical data that were generated through continuous monitoring stations of water quality parameters (i.e. the dependent variable) of Johor River in order to imitate their secondary attribute (i.e. the independent variable). Nevertheless, the data arising from the monitoring stations and experiment might be polluted by noise signals owing to systematic and random errors. This noisy data often made the predicted task relatively difficult. Thus, in order to compensate for this augmented noise, the primary objective of this study was to develop a technique that could enhance the accuracy of water quality prediction (WQP). Therefore, this study proposed an augmented wavelet de-noising technique with Neuro-Fuzzy Inference System (WDT-ANFIS) based on the data fusion module for WQP. The efficiency of the modules was examined to predict critical parameters that were affected by the urbanization surrounding the river. The parameters were investigated in terms of the following: the electrical conductivity (COND), the total dissolved solids (TDSs) and turbidity (TURB). The results showed that the optimum level of accuracy was achieved by making the length of cross-validation equal one-fifth of the data records. Moreover, the WDT-ANFIS module outperformed the ANFIS module with significant improvement in predicting accuracy. This result indicated that the proposed approach was basically an attractive alternative, offering a relatively fast algorithm with good theoretical properties to de-noise and predict the water quality parameters. This new technique would be valuable to assist decision-makers in reporting the status of water quality, as well as investigating spatial and temporal changes.

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring

We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R2), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.

Application of artificial neural networks for water quality prediction

The term “water quality” is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.

Amplified wavelet-ANFIS-based model for GPS/INS integration to enhance vehicular navigation system

Inertial navigation system (INS) relying on gyroscopes and accelerometers has been recently utilized in land vehicles. These INS sensors are integrated with Global Positioning System (GPS) to provide reliable positioning solutions in case of GPS outages that commonly occur in urban canyons. The major inadequacies of INS navigation sensors are the high noise level and the large bias instabilities that are stochastic in nature. The effects of these inadequacies manifest themselves as large position errors during GPS outages. Wavelet analysis is a signal processing method which is recently auspicious by many researchers due to its advantageous adaptation to non-stationary signals and able to perform analysis in both time and frequency domain over other signal processing methods such as the fast Fourier transform in some fields. This research proposes the utilization of wavelet de-nosing to improve the signal-to-noise ratio of each of the INS sensors. In addition, a neuro-fuzzy module is used to provide a reliable prediction of the vehicle position during GPS outages. The results from a road test experiment show the effectiveness of the proposed wavelet—neuro-fuzzy module.

Application of Neural Network for Scour and Air Entrainment Prediction

This research aims at introducing a system independent method for scour and air entrainment prediction utilizing Artificial Neural Network (ANN) based on previous experimental plunge pool scour tests for inclined circular jets. Furthermore, the current manuscript introduced a single ANN model to predict air entrainment devoid of pre-knowledge of the jet condition either smooth or rough jet. Regarding ANN applicability validation, its prediction results was compared to the earlier experimental results for three regression models; one for scour, and two air-models for a smooth and rough jet. The results from each model out of the three ANN models are proved more accurate than the corresponding pre-developed regression models. Relative error envelop of 5% was found to bound all the records for the prediction of air in both ANN models (smooth and rough). For the prediction of the scour, the ANN model was also better than the regression model with only two data records of 20% relative error.

A GIS-ANN-Based Approach for Enhancing the Effect of Slope in the Modified Green-Ampt Model

Most infiltration models survey infiltration in large scale regions using an assumption that the slope of the ground is equal to zero. The Modified Green and Ampt model is one of a few infiltration models that considers slope as an input parameter in its formulation. Here, using artificial neural networks in a raster-based design, basic research is presented regarding the effect of surface slope on infiltration. For the investigation, three catchments with different areas and slopes were selected as case studies, based on existing runoff stations in the upstream region of the Johor River Basin in southern Malaysia. In this research, the efficiency of six different functions was studied in order to determine the best performer for slope in the Modified Green and Ampt model. We also sought to find the most suitable ANN transfer function for infiltration calculations. By calculating runoff for each pixel, accumulation maps were used for corroborating the suitability of the obtained results. The results indicated that the Log-sigmoid was the most appropriate transfer function. We also determined that using the exponential form for the slope in the Modified Green and Ampt model formulation was more accurate, as compared to the original linear shape.

Submerged breakwater hydrodynamic modeling for wave dissipation and coral restorer structure

Wave transmission is the coastal character resulted from interaction of incident wave and submerged breakwater. Coastal hydrodynamic parameters mainly wave period, wave height and water depth while structural geometry factor such as structural height, bottom width as well as crest width influenced magnitude of transmitted wave. A controlled systematic test program was undertaken in a monochromatic regular wave condition represented by transmission coefficient, Ct as the reference index of breakwaters wave dissipation in the effect of water depth and incident wave height which indicated high wave suppresion and capability of wave breaking and structural friction to wave motion. This submerged breakwater also an artificial reef which protect and converse marine biology as well as enhance the marine ecological environment.

Assessment of coastal inundation of low lying areas due to sea level rise

Sea level rise due to climate change have a profound impact on low lying coastal zones. The objective of this study is to identify the potential of coastal inundation area due to Sea Level Rise (SLR) along of Cherating to Pekan coast. The shoreline of Pahang has been undergoing severe erosion and inundated by sea water in some locations, hence affects the socio-economy and the livelihood of the coastal communities. Numerical modelling using the MIKE 21 FM software was done to predict coastal inundation for the years 2020 and 2080 along the Cherating to Pekan shoreline by using the condition for the year 2017 as the baseline. The results of the statistical analysis of this numerical model is congruous with the measured data, such as tide, current, and wave direction. The results show that about 17 to 22% of the Cherating to Pekan shoreline is at risk of being inundated due to the projected SLR for the years 2020 and 2080. The map for projected inundation shows that the infrastructure located in the 1km buffers zone from the shoreline will be affected by the sea level rise. This information could be of benefit to all parties involved in ensuring effective coastal management and making preparation and plan to and protect the coastal areas from the potential impacts of climate change and future disasters.

Study the potential of marine energy in the coastal of Selangor and Perak

This study seeks to identify areas with potential new marine energy in the west coast of Peninsular Malaysia, focusing on the states of Perak and Selangor. The sources of marine energy discussed in this study are from the wave and tidal. The data relating to the new marine energy was collected from the related government departments involved and then was analyzed by using Microsoft Excel software to plot graphs of all the non-spatial data. This study also utilised GIS applications to display the potential for wave energy in the specified area. The spatial analysis showed that the despite high potential of harnessing marine energy in the study area and need the support from the power generating equipment to enhance the energy power. From this study, it is concluded that the potential for renewable marine energy from waves and tidal is at the waters of Permatang Sedepa and Port Klang areas.

Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS) (Water Resour Manage, 10.1007/s11269-013-0382-4)

Reservoir planning and management are critical to the development of the hydrological field and necessary to Integrated Water Resources Management. The growth of forecasting models has resulted in an excellent model known as the Support Vector Machine (SVM). This model uses linearly separable patterns based on an optimal hyperplane, which are extended to non-linearly separable patterns by transforming the raw data to map into a new space. SVM can find a global optimal solution equipped with Kernel functions. These Kernel functions have high flexibility in the forecasting computation, enabling data to be mapped at a higher and infinite-dimensional space in an implicit manner. This paper presents a new solution to the expert system, using SVM to forecast the daily dam water level of the Klang gate. Four categories are identified to determine the best model: the input scenario, the type of SVM regression, the number of V-fold cross-validation and the time lag. The best input scenario employs both the rainfall R(t-i) and the dam water level L(t-i). Type 2 SVM regression is selected as the best regression type, and 5-fold cross-validation produces the most accurate results. The results are compared with those obtained using ANFIS: all the RMSE, MAE and MAPE values prove that SVM is a superior model to ANFIS. Finally, all the results are combined to determine the best time lag, resulting in R(t-2) L(t-2) for the best model with only 1.64 % error. Copyright Springer Science+Business Media Dordrecht 2013